Deep infrared pedestrian classification based on automatic image matting

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

8 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)484-496
Journal / PublicationApplied Soft Computing Journal
Volume77
Online published31 Jan 2019
Publication statusPublished - Apr 2019

Abstract

Infrared pedestrian classification plays an important role in advanced driver assistance systems. However, it encounters great difficulties when the pedestrian images are superimposed on a cluttered background. Many researchers design very deep neural networks to classify pedestrian from cluttered background. However, a very deep neural network associated with a high computational cost. The suppression of cluttered background can boost the performance of deep neural networks without increasing their depth, while it has received little attention in the past. This study presents an automatic image matting approach for infrared pedestrians that suppresses the cluttered background and provides consistent input to deep learning. The domain expertise in pedestrian classification is applied to automatically and softly extract foreground objects from images with cluttered backgrounds. This study generates trimaps, which must be generated manually in conventional approaches, according to the estimated positions of pedestrian's head and upper body without the need for any user interaction. We implement image matting by adopting the global matting approach and taking the generated trimap as an input. The representation of pedestrian is discovered by a deep learning approach from the resulting alpha mattes in which cluttered background is suppressed, and foreground is enhanced. The experimental results show that the proposed approach improves the infrared pedestrian classification performance of the state-of-the-art deep learning approaches at a negligible computational cost.

Research Area(s)

  • Deep learning, Image matting, Infrared image, Pedestrian classification

Bibliographic Note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Citation Format(s)

Deep infrared pedestrian classification based on automatic image matting. / Liang, Yihui; Huang, Han; Cai, Zhaoquan; Hao, Zhifeng; Tan, Kay Chen.

In: Applied Soft Computing Journal, Vol. 77, 04.2019, p. 484-496.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal